In [1]:
import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt
In [2]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [3]:
stocks = px.data.stocks()
stocks.head()
Out[3]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [4]:
import numpy as np

p = plt.figure()

p.set_figwidth(10)
p.set_figheight(10)


plt.plot(stocks['date'],stocks['GOOG'])
plt.title('Google stocks')
plt.xticks(np.arange(0,len(stocks['date'])+1,step = 14))
plt.ylabel('stock value')
plt.xlabel('date')



plt.show()

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [5]:
stocks.plot(x ='date', figsize = (10,10), style = ['-1','.1','-.3',':4','--8','-s'])
plt.ylabel('stock value')

plt.show()

Seaborn¶

First, load the tips dataset

In [6]:
tips = sns.load_dataset('tips')
tips.head()
Out[6]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [7]:
#Are there differences between smokers and non-smokers it comes to giving tips?
from scipy import stats
xrs = tips[tips['smoker'] == 'Yes']['total_bill'] 
yrs = tips[tips['smoker'] == 'Yes']['tip']

xrns = tips[tips['smoker'] == 'No']['total_bill']
yrns = tips[tips['smoker'] == 'No']['tip']


slopes, intercepts, rs, ps, std_errs = stats.linregress(xrs, yrs)
slopens, interceptns, rns, pns, std_errns = stats.linregress(xrns, yrns)

sns.scatterplot(x='total_bill', y='tip', data=tips, hue="smoker")
sns.lineplot(x = xrs,y = xrs*slopes+intercepts, data=tips, hue = 'smoker')
sns.lineplot(x = xrns,y = xrns*slopens+interceptns, data=tips)


plt.show()

Plotly Express¶

Question 4:¶

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [8]:
df = px.data.stocks() 
fig = px.line(df, x="date", y=["GOOG",'AAPL','AMZN','FB','NFLX','MSFT'], markers = True )
fig.show()

The tips dataset¶

In [9]:
df2 = px.data.tips() 
fig2 = px.scatter(df2, x="total_bill", y="tip", color= 'smoker')


fig2.show()

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [10]:
#load data
df3 = px.data.gapminder()
df3.head()
Out[10]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [11]:
df4 = df3[df3['year'] == 2007]
y=df4.groupby('continent').sum()
fig3 = px.bar(y,'pop' ,color = ['Asia','Ociania','Europe','Amerika','Afrika'], title = 'population', text = 'pop')
fig3.update_yaxes(categoryorder= "total ascending")


fig3.show()
y
Out[11]:
year lifeExp pop gdpPercap iso_num
continent
Africa 104364 2849.914 929539692 160629.695446 23859
Americas 50175 1840.203 898871184 275075.790634 9843
Asia 66231 2334.040 3811953827 411609.886714 13354
Europe 60210 2329.458 586098529 751634.449078 12829
Oceania 4014 161.439 24549947 59620.376550 590
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